Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Exhibit
Refer to the exhibit. ``` System Instruction: You are a helpful assistant. Prompt: Tell me about the Eiffel Tower. Response: The Eiffel Tower is located in Paris, France. It is 330 meters tall. ```
A developer uses a generative AI model with the system instruction shown. The response is correct but very brief. Which parameter adjustment could encourage more detail without losing accuracy?
⚠ Common exam trap
The Google Gen AI Leader exam often tests the misconception that increasing randomness (temperature) or restricting token selection (topK) can improve detail, when in fact these parameters trade off accuracy for diversity or determinism, and the correct approach is to use prompt engineering to guide output length and style.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add 'Provide a detailed response' to the system instruction.
Modifying the system instruction to explicitly request a detailed response directly influences the model's output behavior without altering its underlying probability distribution. This approach preserves accuracy by keeping temperature, topK, and other sampling parameters at their default values, ensuring the model remains faithful to the training data while simply prompting for more elaboration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add 'Provide a detailed response' to the system instruction.
Why this is correct
Adding an explicit instruction for detail to the system instruction steers the model's generation toward longer, more thorough responses while the underlying task and data remain unchanged, preserving accuracy. This directly addresses the brevity without altering model parameters.
- ✗
Set temperature to 0 to make output deterministic.
Why it's wrong here
Temperature 0 makes sampling greedy and deterministic, which shortens and repeats phrasing rather than adding detail; it controls randomness, not length. It would be the right choice when reproducible, identical outputs are required, such as regression testing or structured extraction.
- ✗
Set topK to 1 to focus on most likely tokens.
Why it's wrong here
topK=1 restricts sampling to the single highest-probability token, producing terse, repetitive output. topK limits the candidate pool, not verbosity. It would be correct when strictly factual, low-variance answers are wanted, such as classification or deterministic Q&A.
- ✗
Increase temperature to 1.5 to encourage creativity.
Why it's wrong here
Temperature 1.5 flattens the probability distribution, producing incoherent or hallucinated text and risking the accuracy the stem requires. Temperature governs randomness, not response length. It would suit brainstorming or creative writing where diverse, unexpected phrasing is desirable.
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